website: some typos

docs/concepts/GAMBLR_family.html

@@ -257,7 +257,7 @@ <h1 class="title">GAMBLR family of packages</h1>
 <p>GAMBLR.viz is the top-most member of the <a href="(https://github.com/morinlab/GAMBLR)">GAMBLR</a> family of packages that is designed to be used outside of GSC and for users who are not members of Morin Lab. It mostly serves the purpose of providing functions for high-level visualizations of genomic data, including simple somatic mutations, structural variations, copy number alterations, and combinations of these data.</p>
 <p>The GAMBLR family includes:</p>
 <ul>
-<li><a href="https://github.com/morinlab/GAMBLR.viz">GAMBLR.data</a> - collection of genomic data for analysis of Mature B-cell neoplasms</li>
+<li><a href="https://github.com/morinlab/GAMBLR.data">GAMBLR.data</a> - collection of genomic data for analysis of Mature B-cell neoplasms</li>
 <li><a href="https://github.com/morinlab/GAMBLR.helpers">GAMBLR.helpers</a> - a set of low-level functions for data operation</li>
 <li><a href="https://github.com/morinlab/GAMBLR.utils">GAMBLR.utils</a> - higher level set of functions to operate on genomic data</li>
 <li>GAMBLR.viz - this package</li>

docs/concepts/GAMBLR_family.qmd

@@ -12,7 +12,7 @@ number alterations, and combinations of these data.
 
 The GAMBLR family includes:
 
-* [GAMBLR.data](https://github.com/morinlab/GAMBLR.viz) - collection of genomic
+* [GAMBLR.data](https://github.com/morinlab/GAMBLR.data) - collection of genomic
 data for analysis of Mature B-cell neoplasms
 * [GAMBLR.helpers](https://github.com/morinlab/GAMBLR.helpers) - a set of
 low-level functions for data operation

docs/search.json

@@ -32,7 +32,7 @@
     "href": "tutorials/data_q.html",
     "title": "Tutorial: Exploring data quality",
     "section": "",
-    "text": "When comparing the mutation data from different sources, it is important to consider different underlying biases that may lead to unexpected results or incorrect conclusions. Different centers and groups may have different acceptable standards to library preparation, sequencing, data quality control, variant calling and analysis. More discussion on this can be found in the Dreval et al Blood spotlight published in 2022. Can we use GAMBLR.viz to conduct similar investigation and take advantage of the bundled data and the visualizations available with this package? This tutorial will demonstate the example of the inputs and showcase the main features of such analysis, replicating some panels and figures from that study."
+    "text": "When comparing the mutation data from different sources, it is important to consider different underlying biases that may lead to unexpected results or incorrect conclusions. Different centers and groups may have different acceptable standards to library preparation, sequencing, data quality control, variant calling and analysis. More discussion on this can be found in the Dreval et al Blood spotlight published in 2022.  Can we use GAMBLR.viz to conduct similar investigation and take advantage of the bundled data and the visualizations available with this package?  This tutorial will demonstate the example of the inputs and showcase the main features of such analysis, replicating some panels and figures from that study."
   },
   {
     "objectID": "tutorials/data_q.html#prepare-setup",
@@ -53,7 +53,7 @@
     "href": "tutorials/data_q.html#correlation-between-coverage-and-mutation-discovery",
     "title": "Tutorial: Exploring data quality",
     "section": "Correlation between coverage and mutation discovery",
-    "text": "Correlation between coverage and mutation discovery\nNext, we can see whether there is any limitation to detect somatic mutations in the relevant genes when the sample is of a very poor quality. Indded, the plot below replicates the panel C of Figure 1 in the manuscript and shows that there is direct effect:\n\n# reformat naming of the standard colors\ncols <- get_gambl_colours()[c(\"Schmitz\", \"Chapuy\", \"Reddy\")]\nnames(cols) <- c(\"Schmitz, 2018\", \"Chapuy, 2018\", \"Reddy, 2017\")\n\n# plot\np <- metadata %>%\n    ggplot(\n        aes(\n            x = MeanCorrectedCoverage,\n            y = coding_mutations,\n            colour = study\n        )\n    ) +\n    geom_point(alpha = 0.5) +\n    scale_color_manual(values = cols) +\n    ylim(c(0, 1000)) +\n    xlim(c(0, 200)) +\n    theme_Morons(\n        base_size = 8,\n        my_legend_position = \"bottom\",\n        my_legend_direction = \"horizontal\"\n    )\n\n# display density\nggMarginal(\n    p,\n    type = \"density\",\n    groupColour = TRUE,\n    groupFill = TRUE\n)"
+    "text": "Correlation between coverage and mutation discovery\nNext, we can see whether there is any limitation to detect somatic mutations in the relevant genes when the sample is of a very poor quality. Indeed, the plot below replicates the panel C of Figure 1 in the manuscript and shows that there is direct effect:\n\n# reformat naming of the standard colors\ncols <- get_gambl_colours()[c(\"Schmitz\", \"Chapuy\", \"Reddy\")]\nnames(cols) <- c(\"Schmitz, 2018\", \"Chapuy, 2018\", \"Reddy, 2017\")\n\n# plot\np <- metadata %>%\n    ggplot(\n        aes(\n            x = MeanCorrectedCoverage,\n            y = coding_mutations,\n            colour = study\n        )\n    ) +\n    geom_point(alpha = 0.5) +\n    scale_color_manual(values = cols) +\n    ylim(c(0, 1000)) +\n    xlim(c(0, 200)) +\n    theme_Morons(\n        base_size = 8,\n        my_legend_position = \"bottom\",\n        my_legend_direction = \"horizontal\"\n    )\n\n# display density\nggMarginal(\n    p,\n    type = \"density\",\n    groupColour = TRUE,\n    groupFill = TRUE\n)"
   },
   {
     "objectID": "tutorials/data_q.html#find-significant-differences-between-low-and-high-coverage-groups",

docs/tutorials/data_q.html

@@ -312,7 +312,7 @@ <h1 class="title">Tutorial: Exploring data quality</h1>
 
 </header>
 
-<p>When comparing the mutation data from different sources, it is important to consider different underlying biases that may lead to unexpected results or incorrect conclusions. Different centers and groups may have different acceptable standards to library preparation, sequencing, data quality control, variant calling and analysis. More discussion on this can be found in the <a href="https://doi.org/10.1182/blood.2022016095">Dreval et al</a> Blood spotlight published in 2022. Can we use GAMBLR.viz to conduct similar investigation and take advantage of the bundled data and the visualizations available with this package? This tutorial will demonstate the example of the inputs and showcase the main features of such analysis, replicating some panels and figures from that study.</p>
+<p>When comparing the mutation data from different sources, it is important to consider different underlying biases that may lead to unexpected results or incorrect conclusions. Different centers and groups may have different acceptable standards to library preparation, sequencing, data quality control, variant calling and analysis. More discussion on this can be found in the <a href="https://doi.org/10.1182/blood.2022016095">Dreval et al</a> Blood spotlight published in 2022. <br> Can we use GAMBLR.viz to conduct similar investigation and take advantage of the bundled data and the visualizations available with this package? <br> This tutorial will demonstate the example of the inputs and showcase the main features of such analysis, replicating some panels and figures from that study.</p>
 <section id="prepare-setup" class="level2">
 <h2 class="anchored" data-anchor-id="prepare-setup">Prepare setup</h2>
 <p>We will first import the necessary packages:</p>
@@ -384,7 +384,7 @@ <h2 class="anchored" data-anchor-id="target-space-coverage-across-the-studies">T
 </section>
 <section id="correlation-between-coverage-and-mutation-discovery" class="level2">
 <h2 class="anchored" data-anchor-id="correlation-between-coverage-and-mutation-discovery">Correlation between coverage and mutation discovery</h2>
-<p>Next, we can see whether there is any limitation to detect somatic mutations in the relevant genes when the sample is of a very poor quality. Indded, the plot below replicates the panel C of Figure 1 in the manuscript and shows that there is direct effect:</p>
+<p>Next, we can see whether there is any limitation to detect somatic mutations in the relevant genes when the sample is of a very poor quality. Indeed, the plot below replicates the panel C of Figure 1 in the manuscript and shows that there is direct effect:</p>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># reformat naming of the standard colors</span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>cols <span class="ot">&lt;-</span> <span class="fu">get_gambl_colours</span>()[<span class="fu">c</span>(<span class="st">"Schmitz"</span>, <span class="st">"Chapuy"</span>, <span class="st">"Reddy"</span>)]</span>

docs/tutorials/data_q.qmd

@@ -14,8 +14,10 @@ acceptable standards to library preparation, sequencing, data quality control,
 variant calling and analysis. More discussion on this can be found in the
 [Dreval et al](https://doi.org/10.1182/blood.2022016095) Blood spotlight
 published in 2022.
+<br>
 Can we use GAMBLR.viz to conduct similar investigation and take advantage of
 the bundled data and the visualizations available with this package?
+<br>
 This tutorial will demonstate the example of the inputs and showcase the main
 features of such analysis, replicating some panels and figures from that study.
 
@@ -114,7 +116,7 @@ quality affects the ability to detect coding mutations in lymphoma genes?
 ## Correlation between coverage and mutation discovery
 
 Next, we can see whether there is any limitation to detect somatic mutations in
-the relevant genes when the sample is of a very poor quality. Indded, the plot
+the relevant genes when the sample is of a very poor quality. Indeed, the plot
 below replicates the panel C of Figure 1 in the manuscript and shows that there
 is direct effect:
 ```{r}